{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TensorFlow Tutorial #18\n",
    "# TFRecords & Dataset API\n",
    "\n",
    "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)/[GitHub中文](https://github.com/Hvass-Labs/TensorFlow-Tutorials-Chinese)\n",
    "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube](https://www.youtube.com/playlist?list=PL9Hr9sNUjfsmEu1ZniY0XpHSzl5uihcXZ)\n",
    "\n",
    "中文翻译[ZhouGeorge](https://github.com/ZhouGeorge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 介绍\n",
    "\n",
    "在之前的教程中我们使用一个称为feed-dict的方法来给Tensorflow图中输入数据。这是相当简单的输入方法，但是它存在一个性能瓶颈，因为数据是在训练步数之间相继读取的。这使得GPU的使用率很难到达100%，因为GPU需要等待新的数据读取后才能工作。\n",
    "\n",
    "相反，我们想要在一个并行线程中读取数据，只要GPU准备就绪时总是有新的训练数据可用的。以前需要用到TensorFlow中称为 QueueRunners的方法，这是一个非常复杂的系统。现在可以通过Dataset API和一个称为TFRecords的二进制格式文件来完成，如本教程所述。\n",
    "\n",
    "这是在教程 #17的基础上进行的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/magnus/anaconda3/envs/tf-gpu/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6\n",
      "  return f(*args, **kwds)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.image import imread\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import sys\n",
    "import os"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "开发环境 PYthon3.6 和 TensorFlow 版本："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.4.0'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 加载数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import knifey"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据的维度已经在`knifey`模块中定义，所以我们只需要引用我们需要的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from knifey import img_size, img_size_flat, img_shape, num_classes, num_channels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在你的电脑上设置存储数据集的路径"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# knifey.data_dir = \"data/knifey-spoony/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Knifey-Spoony数据集大约22MB，如果本地路径里没有，会被自动的下载。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data has apparently already been downloaded and unpacked.\n"
     ]
    }
   ],
   "source": [
    "knifey.maybe_download_and_extract()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在加载数据集。下面扫描子目录所有 `*.jpg`的图像并将文件名放入两个列表作为训练集和测试集。这里并没有将图像加载到内存中。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating dataset from the files in: data/knifey-spoony/\n",
      "- Data loaded from cache-file: data/knifey-spoony/knifey-spoony.pkl\n"
     ]
    }
   ],
   "source": [
    "dataset = knifey.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获得类别的名称。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['forky', 'knifey', 'spoony']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class_names = dataset.class_names\n",
    "class_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练和测试设置"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个函数返回文件下的图像，整数形的类别数，和标签的独热编码数组类型。\n",
    "\n",
    "在这个教程中我们将实际使用整数形的类别作为标签。这也许有点让人困惑，您总是可以添加打印语句来查看实际的数据是什么。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_paths_train, cls_train, labels_train = dataset.get_training_set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印第一个图像的路径检测是否正确。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/magnus/development/TensorFlow-Tutorials/data/knifey-spoony/forky/forky-05-0023.jpg'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_paths_train[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "获得测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "image_paths_test, cls_test, labels_test = dataset.get_test_set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印第一个图像路径检测是否正确。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/magnus/development/TensorFlow-Tutorials/data/knifey-spoony/forky/test/forky-test-01-0163.jpg'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_paths_test[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Knifey-Spoony数据集已经被加载，它包含4700张图像和相应的标签。数据集被划分成2个子集，训练集和测试集。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of:\n",
      "- Training-set:\t\t4170\n",
      "- Test-set:\t\t530\n"
     ]
    }
   ],
   "source": [
    "print(\"Size of:\")\n",
    "print(\"- Training-set:\\t\\t{}\".format(len(image_paths_train)))\n",
    "print(\"- Test-set:\\t\\t{}\".format(len(image_paths_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 用于画图的辅助函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在3x3的网格中画出9个图像，并且在每张图像的下方写出正确的类和预测的类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_images(images, cls_true, cls_pred=None, smooth=True):\n",
    "\n",
    "    assert len(images) == len(cls_true)\n",
    "\n",
    "    # Create figure with sub-plots.\n",
    "    fig, axes = plt.subplots(3, 3)\n",
    "\n",
    "    # Adjust vertical spacing.\n",
    "    if cls_pred is None:\n",
    "        hspace = 0.3\n",
    "    else:\n",
    "        hspace = 0.6\n",
    "    fig.subplots_adjust(hspace=hspace, wspace=0.3)\n",
    "\n",
    "    # Interpolation type.\n",
    "    if smooth:\n",
    "        interpolation = 'spline16'\n",
    "    else:\n",
    "        interpolation = 'nearest'\n",
    "\n",
    "    for i, ax in enumerate(axes.flat):\n",
    "        # There may be less than 9 images, ensure it doesn't crash.\n",
    "        if i < len(images):\n",
    "            # Plot image.\n",
    "            ax.imshow(images[i],\n",
    "                      interpolation=interpolation)\n",
    "\n",
    "            # Name of the true class.\n",
    "            cls_true_name = class_names[cls_true[i]]\n",
    "\n",
    "            # Show true and predicted classes.\n",
    "            if cls_pred is None:\n",
    "                xlabel = \"True: {0}\".format(cls_true_name)\n",
    "            else:\n",
    "                # Name of the predicted class.\n",
    "                cls_pred_name = class_names[cls_pred[i]]\n",
    "\n",
    "                xlabel = \"True: {0}\\nPred: {1}\".format(cls_true_name,\n",
    "                                                       cls_pred_name)\n",
    "\n",
    "            # Show the classes as the label on the x-axis.\n",
    "            ax.set_xlabel(xlabel)\n",
    "        \n",
    "        # Remove ticks from the plot.\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "    \n",
    "    # Ensure the plot is shown correctly with multiple plots\n",
    "    # in a single Notebook cell.\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加载图像的辅助函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个数据集没有加载实际的图像，替代的是一个训练集图像的列表和另一个测试集图像的列表。这个辅助行数是用于加载一些图像文件。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def load_images(image_paths):\n",
    "    # Load the images from disk.\n",
    "    images = [imread(path) for path in image_paths]\n",
    "\n",
    "    # Convert to a numpy array and return it.\n",
    "    return np.asarray(images)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 画出一些数据检测数据是否正确"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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I2agQT1JqahM5g5PHu8w6fSpGDt/Is7m8yh3X+xtX+9kiyzJGoxFxHLG4uEg+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\nkkuAy8nwkksuuQSA/xeciVsSrGqkOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe813303c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the first images from the test-set.\n",
    "images = load_images(image_paths=image_paths_test[0:9])\n",
    "\n",
    "# Get the true classes for those images.\n",
    "cls_true = cls_test[0:9]\n",
    "\n",
    "# Plot the images and labels using our helper-function above.\n",
    "plot_images(images=images, cls_true=cls_true, smooth=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 创建 TFRecords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TFRecords是一个在TensorFlow内部使用二进制格式文件，它可以帮助高效读取和处理数据集。\n",
    "\n",
    "对于这个小的数据集我们只需要为训练集和测试集各创建一个TFRecords文件，但是如果你的数据集非常大，那么你需要创建一些TFRecords文件，称为碎片。它也提供随机清洗，因为Dataset API只能从一个较小的缓冲区中进行清洗，例如：被加载到RAM中的1024个元素。所以如果你有100个TFReocrd文件，将其随机化将会比读取单个TFRecords文件好的多。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "持有训练集的TFRecords的文件路径。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/knifey-spoony/train.tfrecords'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_tfrecords_train = os.path.join(knifey.data_dir, \"train.tfrecords\")\n",
    "path_tfrecords_train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "持有测试集的TFRecords的文件路径。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'data/knifey-spoony/test.tfrecords'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_tfrecords_test = os.path.join(knifey.data_dir, \"test.tfrecords\")\n",
    "path_tfrecords_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "打印转换进度的辅助函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_progress(count, total):\n",
    "    # Percentage completion.\n",
    "    pct_complete = float(count) / total\n",
    "\n",
    "    # Status-message.\n",
    "    # Note the \\r which means the line should overwrite itself.\n",
    "    msg = \"\\r- Progress: {0:.1%}\".format(pct_complete)\n",
    "\n",
    "    # Print it.\n",
    "    sys.stdout.write(msg)\n",
    "    sys.stdout.flush()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用于打包成整形，这样可以被保存到TFRecords文件中的辅助函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def wrap_int64(value):\n",
    "    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用于打包成原始字节，这样可以被保存到TFRecords文件中的辅助函数。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def wrap_bytes(value):\n",
    "    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个函数用于从硬盘中读取图像并和它们的标签一起写入TFRecords文件。这将加载并将图像解码成numpy数组，然后以原始字节的方式存入TFRecords文件。如果原始图像文件被压缩，例如JPEG文件，那么TFRecords文件可能比原始图像文件大几倍。\n",
    "\n",
    "当然也可以将被压缩的图像直接存入TFRecoards，因为它可以容纳任意原始字节。但当在之后利用下面`parse()`读取TFRecord时，我们必须对压缩的图像解码。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def convert(image_paths, labels, out_path):\n",
    "    # Args:\n",
    "    # image_paths   List of file-paths for the images.\n",
    "    # labels        Class-labels for the images.\n",
    "    # out_path      File-path for the TFRecords output file.\n",
    "    \n",
    "    print(\"Converting: \" + out_path)\n",
    "    \n",
    "    # Number of images. Used when printing the progress.\n",
    "    num_images = len(image_paths)\n",
    "    \n",
    "    # Open a TFRecordWriter for the output-file.\n",
    "    with tf.python_io.TFRecordWriter(out_path) as writer:\n",
    "        \n",
    "        # Iterate over all the image-paths and class-labels.\n",
    "        for i, (path, label) in enumerate(zip(image_paths, labels)):\n",
    "            # Print the percentage-progress.\n",
    "            print_progress(count=i, total=num_images-1)\n",
    "\n",
    "            # Load the image-file using matplotlib's imread function.\n",
    "            img = imread(path)\n",
    "            \n",
    "            # Convert the image to raw bytes.\n",
    "            img_bytes = img.tostring()\n",
    "\n",
    "            # Create a dict with the data we want to save in the\n",
    "            # TFRecords file. You can add more relevant data here.\n",
    "            data = \\\n",
    "                {\n",
    "                    'image': wrap_bytes(img_bytes),\n",
    "                    'label': wrap_int64(label)\n",
    "                }\n",
    "\n",
    "            # Wrap the data as TensorFlow Features.\n",
    "            feature = tf.train.Features(feature=data)\n",
    "\n",
    "            # Wrap again as a TensorFlow Example.\n",
    "            example = tf.train.Example(features=feature)\n",
    "\n",
    "            # Serialize the data.\n",
    "            serialized = example.SerializeToString()\n",
    "            \n",
    "            # Write the serialized data to the TFRecords file.\n",
    "            writer.write(serialized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请注意，要将数据写入到TFRecords文件中需要的调用4个函数。在原始的谷歌开发者的样例代码中，这四个函数调用实际上是嵌套的。TensorFlow的设计理念似乎是：如果一个函数调用是好的，那么调用4个函数是4倍的好，如果它们是嵌套的那么它就是指数级的好！\n",
    "\n",
    "当然，这是相当糟糕的API设计，因为最后一个函数 `writer.write()`应该只要能够直接使用数据字典就可以了并且内部还调用了其他3个函数\n",
    "\n",
    "将训练集数据转换到TFRecords文件。注意我们是如何用整形类别标签来代替独热编码数组的。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting: data/knifey-spoony/train.tfrecords\n",
      "- Progress: 100.0%"
     ]
    }
   ],
   "source": [
    "convert(image_paths=image_paths_train,\n",
    "        labels=cls_train,\n",
    "        out_path=path_tfrecords_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将测试集数据转换到TFRecords："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting: data/knifey-spoony/test.tfrecords\n",
      "- Progress: 100.0%"
     ]
    }
   ],
   "source": [
    "convert(image_paths=image_paths_test,\n",
    "        labels=cls_test,\n",
    "        out_path=path_tfrecords_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimator的输入函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TFRecords文件包括序列化的二值格式数据，需要被转换回图像和正确的标签。我们用辅助函数来进行解析："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parse(serialized):\n",
    "    # Define a dict with the data-names and types we expect to\n",
    "    # find in the TFRecords file.\n",
    "    # It is a bit awkward that this needs to be specified again,\n",
    "    # because it could have been written in the header of the\n",
    "    # TFRecords file instead.\n",
    "    features = \\\n",
    "        {\n",
    "            'image': tf.FixedLenFeature([], tf.string),\n",
    "            'label': tf.FixedLenFeature([], tf.int64)\n",
    "        }\n",
    "\n",
    "    # Parse the serialized data so we get a dict with our data.\n",
    "    parsed_example = tf.parse_single_example(serialized=serialized,\n",
    "                                             features=features)\n",
    "\n",
    "    # Get the image as raw bytes.\n",
    "    image_raw = parsed_example['image']\n",
    "\n",
    "    # Decode the raw bytes so it becomes a tensor with type.\n",
    "    image = tf.decode_raw(image_raw, tf.uint8)\n",
    "    \n",
    "    # The type is now uint8 but we need it to be float.\n",
    "    image = tf.cast(image, tf.float32)\n",
    "\n",
    "    # Get the label associated with the image.\n",
    "    label = parsed_example['label']\n",
    "\n",
    "    # The image and label are now correct TensorFlow types.\n",
    "    return image, label"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了使用Estimator API，创建用于从TFRecord文件读取数据input-function的辅助函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def input_fn(filenames, train, batch_size=32, buffer_size=2048):\n",
    "    # Args:\n",
    "    # filenames:   Filenames for the TFRecords files.\n",
    "    # train:       Boolean whether training (True) or testing (False).\n",
    "    # batch_size:  Return batches of this size.\n",
    "    # buffer_size: Read buffers of this size. The random shuffling\n",
    "    #              is done on the buffer, so it must be big enough.\n",
    "\n",
    "    # Create a TensorFlow Dataset-object which has functionality\n",
    "    # for reading and shuffling data from TFRecords files.\n",
    "    dataset = tf.data.TFRecordDataset(filenames=filenames)\n",
    "\n",
    "    # Parse the serialized data in the TFRecords files.\n",
    "    # This returns TensorFlow tensors for the image and labels.\n",
    "    dataset = dataset.map(parse)\n",
    "\n",
    "    if train:\n",
    "        # If training then read a buffer of the given size and\n",
    "        # randomly shuffle it.\n",
    "        dataset = dataset.shuffle(buffer_size=buffer_size)\n",
    "\n",
    "        # Allow infinite reading of the data.\n",
    "        num_repeat = None\n",
    "    else:\n",
    "        # If testing then don't shuffle the data.\n",
    "        \n",
    "        # Only go through the data once.\n",
    "        num_repeat = 1\n",
    "\n",
    "    # Repeat the dataset the given number of times.\n",
    "    dataset = dataset.repeat(num_repeat)\n",
    "    \n",
    "    # Get a batch of data with the given size.\n",
    "    dataset = dataset.batch(batch_size)\n",
    "\n",
    "    # Create an iterator for the dataset and the above modifications.\n",
    "    iterator = dataset.make_one_shot_iterator()\n",
    "\n",
    "    # Get the next batch of images and labels.\n",
    "    images_batch, labels_batch = iterator.get_next()\n",
    "\n",
    "    # The input-function must return a dict wrapping the images.\n",
    "    x = {'image': images_batch}\n",
    "    y = labels_batch\n",
    "\n",
    "    return x, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了使用Estimator API，下面是训练集的input-function："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def train_input_fn():\n",
    "    return input_fn(filenames=path_tfrecords_train, train=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了使用Estimator API，下面是测试集的input-function："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def test_input_fn():\n",
    "    return input_fn(filenames=path_tfrecords_test, train=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测新图像的输入函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "预测新数据也需要一个input-function。作为例子，我们只从测试集中取出一些图像。\n",
    "\n",
    "你可以在这里加载任何你图像。确保它们是TensorFflow模型需要的维度，否者你需要改变一下你的图像。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "some_images = load_images(image_paths=image_paths_test[0:9])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这些图像现在已经被加载到内存中，所以我们利用标准的Estimator API 输入函数。注意到，图像以Uint8的格式被加载，但是输入TensorFlow图中需要float格式，所以还要做一个类型转换。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predict_input_fn = tf.estimator.inputs.numpy_input_fn(\n",
    "    x={\"image\": some_images.astype(np.float32)},\n",
    "    num_epochs=1,\n",
    "    shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在预测的时候不需要正确的类别。然而，我们需要在之后的画图上用到。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "some_images_cls = cls_test[0:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  预制/封装 Estimator\n",
    "\n",
    "当我们使用预制Estimator时，我们需要为数据指定输入特性。在这个例子中，我们希望从我们的data-set中输入图像，它们是给定形状的数字数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_image = tf.feature_column.numeric_column(\"image\",\n",
    "                                                 shape=img_shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你可以有几个输入特性，然后将它们组合在一个列表中："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "feature_columns = [feature_image]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在这个例子中我们想要用3个隐藏层的DNN，分别有512.256，128单元。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "num_hidden_units = [512, 256, 128]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "用`DNNClassifier`构筑我们的神经网络。我们也可以指定激活函数和其他不同的参数（见文档）。这里我们只指定分类的类别数量和checkpoints保存的地址。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial18-1/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fe810e90940>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "model = tf.estimator.DNNClassifier(feature_columns=feature_columns,\n",
    "                                   hidden_units=num_hidden_units,\n",
    "                                   activation_fn=tf.nn.relu,\n",
    "                                   n_classes=num_classes,\n",
    "                                   model_dir=\"./checkpoints_tutorial18-1/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练\n",
    "\n",
    "我们现在可以给定的迭代次数训练模型。这将自动加载并保存checkpoints，这样我们就可以在以后继续训练。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial18-1/model.ckpt.\n",
      "INFO:tensorflow:loss = 943.377, step = 1\n",
      "INFO:tensorflow:global_step/sec: 21.6937\n",
      "INFO:tensorflow:loss = 31.8647, step = 101 (4.614 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 200 into ./checkpoints_tutorial18-1/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 31.5808.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.canned.dnn.DNNClassifier at 0x7fe810e179e8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.train(input_fn=train_input_fn, steps=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评估\n",
    "\n",
    "一旦训练好模型，我们可以在测试集上评估它的性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2017-11-25-09:31:37\n",
      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-1/model.ckpt-200\n",
      "INFO:tensorflow:Finished evaluation at 2017-11-25-09:31:38\n",
      "INFO:tensorflow:Saving dict for global step 200: accuracy = 0.456604, average_loss = 1.07088, global_step = 200, loss = 33.3863\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(input_fn=test_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.45660377,\n",
       " 'average_loss': 1.0708818,\n",
       " 'global_step': 200,\n",
       " 'loss': 33.386318}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification accuracy: 45.66%\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测\n",
    "\n",
    "训练好的模型也可以拿来预测新的数据。\n",
    "\n",
    "注意到在对新数据预测时，TensorFlow图被再次创建并且加载checkpoint一次。如果模型非常大，这开销会是非常大的。\n",
    "\n",
    "不知道为什么Estimator被设计成这种方式，可能是为了总是能加载最近的一个Checkpoint，它也可以很容易地分布在多台计算机上。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = model.predict(input_fn=predict_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-1/model.ckpt-200\n"
     ]
    }
   ],
   "source": [
    "cls = [p['classes'] for p in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_pred = np.array(cls, dtype='int').squeeze()\n",
    "cls_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8dPJDVVWGoy690y5bW1us1FaJNJGaPf3pAQlQKk7TmF5jIhzihmMODp8Qqjql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\nyDJuAMeMGfOR5f8HwXVtFSwXLVgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe80adc0630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images(images=some_images,\n",
    "            cls_true=some_images_cls,\n",
    "            cls_pred=cls_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测整个测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "看起来这个模型可能把所有的图像都归为“spoony”。所以让我们对整个测试集进行预测，通过输入函数我们可以很容易地完成。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(input_fn=test_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Input graph does not contain a QueueRunner. That means predict yields forever. This is probably a mistake.\n",
      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-1/model.ckpt-200\n"
     ]
    }
   ],
   "source": [
    "cls = [p['classes'] for p in predictions]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls_pred = np.array(cls, dtype='int').squeeze()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "测试集一共有530张图像，他们全部被预测为类别2（勺子）。所以这个模型根本不能用于在Knifey-Spoony数据集上的分类。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "530"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(cls_pred == 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 新的Estimator"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如果你不使用内置的Estimator，那么你可以自己创建一个任意TensorFlow 模型。为了完成这个，你首先需要创建如下的几个函数：\n",
    "1. TensorFlow 模型，例如一个卷积神经网络\n",
    "2. 模型的输出。\n",
    "3. 用于模型优化的损失函数。\n",
    "4. 优化的方法。\n",
    "5. 性能指标。\n",
    "\n",
    "这个Estimator可以运行三种模式：训练，评估，或预测。这些代码大体相似，但是预测模式下我们不需要设置损失函数和优化器。\n",
    "\n",
    "这是Estimator API的另一个方面，它被设计的糟糕，就像我们过去用结构体来做ANSI C编程一样。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_fn(features, labels, mode, params):\n",
    "    # Args:\n",
    "    #\n",
    "    # features: This is the x-arg from the input_fn.\n",
    "    # labels:   This is the y-arg from the input_fn.\n",
    "    # mode:     Either TRAIN, EVAL, or PREDICT\n",
    "    # params:   User-defined hyper-parameters, e.g. learning-rate.\n",
    "    \n",
    "    # Reference to the tensor named \"image\" in the input-function.\n",
    "    x = features[\"image\"]\n",
    "\n",
    "    # The convolutional layers expect 4-rank tensors\n",
    "    # but x is a 2-rank tensor, so reshape it.\n",
    "    net = tf.reshape(x, [-1, img_size, img_size, num_channels])    \n",
    "\n",
    "    # First convolutional layer.\n",
    "    net = tf.layers.conv2d(inputs=net, name='layer_conv1',\n",
    "                           filters=32, kernel_size=3,\n",
    "                           padding='same', activation=tf.nn.relu)\n",
    "    net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)\n",
    "\n",
    "    # Second convolutional layer.\n",
    "    net = tf.layers.conv2d(inputs=net, name='layer_conv2',\n",
    "                           filters=32, kernel_size=3,\n",
    "                           padding='same', activation=tf.nn.relu)\n",
    "    net = tf.layers.max_pooling2d(inputs=net, pool_size=2, strides=2)    \n",
    "\n",
    "    # Flatten to a 2-rank tensor.\n",
    "    net = tf.contrib.layers.flatten(net)\n",
    "    # Eventually this should be replaced with:\n",
    "    # net = tf.layers.flatten(net)\n",
    "\n",
    "    # First fully-connected / dense layer.\n",
    "    # This uses the ReLU activation function.\n",
    "    net = tf.layers.dense(inputs=net, name='layer_fc1',\n",
    "                          units=128, activation=tf.nn.relu)    \n",
    "\n",
    "    # Second fully-connected / dense layer.\n",
    "    # This is the last layer so it does not use an activation function.\n",
    "    net = tf.layers.dense(inputs=net, name='layer_fc_2',\n",
    "                          units=num_classes)\n",
    "\n",
    "    # Logits output of the neural network.\n",
    "    logits = net\n",
    "\n",
    "    # Softmax output of the neural network.\n",
    "    y_pred = tf.nn.softmax(logits=logits)\n",
    "    \n",
    "    # Classification output of the neural network.\n",
    "    y_pred_cls = tf.argmax(y_pred, axis=1)\n",
    "\n",
    "    if mode == tf.estimator.ModeKeys.PREDICT:\n",
    "        # If the estimator is supposed to be in prediction-mode\n",
    "        # then use the predicted class-number that is output by\n",
    "        # the neural network. Optimization etc. is not needed.\n",
    "        spec = tf.estimator.EstimatorSpec(mode=mode,\n",
    "                                          predictions=y_pred_cls)\n",
    "    else:\n",
    "        # Otherwise the estimator is supposed to be in either\n",
    "        # training or evaluation-mode. Note that the loss-function\n",
    "        # is also required in Evaluation mode.\n",
    "        \n",
    "        # Define the loss-function to be optimized, by first\n",
    "        # calculating the cross-entropy between the output of\n",
    "        # the neural network and the true labels for the input data.\n",
    "        # This gives the cross-entropy for each image in the batch.\n",
    "        cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels,\n",
    "                                                                       logits=logits)\n",
    "\n",
    "        # Reduce the cross-entropy batch-tensor to a single number\n",
    "        # which can be used in optimization of the neural network.\n",
    "        loss = tf.reduce_mean(cross_entropy)\n",
    "\n",
    "        # Define the optimizer for improving the neural network.\n",
    "        optimizer = tf.train.AdamOptimizer(learning_rate=params[\"learning_rate\"])\n",
    "\n",
    "        # Get the TensorFlow op for doing a single optimization step.\n",
    "        train_op = optimizer.minimize(\n",
    "            loss=loss, global_step=tf.train.get_global_step())\n",
    "\n",
    "        # Define the evaluation metrics,\n",
    "        # in this case the classification accuracy.\n",
    "        metrics = \\\n",
    "        {\n",
    "            \"accuracy\": tf.metrics.accuracy(labels, y_pred_cls)\n",
    "        }\n",
    "\n",
    "        # Wrap all of this in an EstimatorSpec.\n",
    "        spec = tf.estimator.EstimatorSpec(\n",
    "            mode=mode,\n",
    "            loss=loss,\n",
    "            train_op=train_op,\n",
    "            eval_metric_ops=metrics)\n",
    "        \n",
    "    return spec"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建Estimator的一个实例\n",
    "\n",
    "我们可以指定超参数，例如优化器的学习率。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "params = {\"learning_rate\": 1e-4}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以创建这个新的Estimator的一个实例。\n",
    "\n",
    "注意我们没有提供feature-columns，因为它会当`model_fn()`被调用时，从data-functions自动推断出来。\n",
    "\n",
    "在TensorFlow文档中写的不清楚，为什么在上面使用`DNNClassifier`必须指定feature-columns，而这里不需要。 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using default config.\n",
      "INFO:tensorflow:Using config: {'_model_dir': './checkpoints_tutorial18-2/', '_tf_random_seed': None, '_save_summary_steps': 100, '_save_checkpoints_steps': None, '_save_checkpoints_secs': 600, '_session_config': None, '_keep_checkpoint_max': 5, '_keep_checkpoint_every_n_hours': 10000, '_log_step_count_steps': 100, '_service': None, '_cluster_spec': <tensorflow.python.training.server_lib.ClusterSpec object at 0x7fe80a0fbe80>, '_task_type': 'worker', '_task_id': 0, '_master': '', '_is_chief': True, '_num_ps_replicas': 0, '_num_worker_replicas': 1}\n"
     ]
    }
   ],
   "source": [
    "model = tf.estimator.Estimator(model_fn=model_fn,\n",
    "                               params=params,\n",
    "                               model_dir=\"./checkpoints_tutorial18-2/\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 训练\n",
    "\n",
    "现在我们新的Estimator已经被创建，我们可以训练它。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Create CheckpointSaverHook.\n",
      "INFO:tensorflow:Saving checkpoints for 1 into ./checkpoints_tutorial18-2/model.ckpt.\n",
      "INFO:tensorflow:loss = 29.6568, step = 1\n",
      "INFO:tensorflow:global_step/sec: 15.1419\n",
      "INFO:tensorflow:loss = 20.0903, step = 101 (6.605 sec)\n",
      "INFO:tensorflow:Saving checkpoints for 200 into ./checkpoints_tutorial18-2/model.ckpt.\n",
      "INFO:tensorflow:Loss for final step: 3.11824.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.estimator.estimator.Estimator at 0x7fe80a0fba20>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.train(input_fn=train_input_fn, steps=200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 评估\n",
    "\n",
    "模型一旦被训练好，我们可以在测试集上去评估它的性能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Starting evaluation at 2017-11-25-09:32:03\n",
      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-2/model.ckpt-200\n",
      "INFO:tensorflow:Finished evaluation at 2017-11-25-09:32:04\n",
      "INFO:tensorflow:Saving dict for global step 200: accuracy = 0.390566, global_step = 200, loss = 6.8253\n"
     ]
    }
   ],
   "source": [
    "result = model.evaluate(input_fn=test_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': 0.39056605, 'global_step': 200, 'loss': 6.8253026}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification accuracy: 39.06%\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification accuracy: {0:.2%}\".format(result[\"accuracy\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测\n",
    "\n",
    "模型也可以用来在新的数据上进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(input_fn=predict_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-2/model.ckpt-200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_pred = np.array(list(predictions))\n",
    "cls_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SMJpMqNQyBoMB4/EEO28TpgVG4zH1eo1e1AbXw3UkhNSgOLXAce+Q6UtLTGnr3Lv5Ke+/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\nmTR0EyZMeOyZNHQTJkx47Pn/AVw5IEth4SmmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8000484e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_images(images=some_images,\n",
    "            cls_true=some_images_cls,\n",
    "            cls_pred=cls_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 预测整个测试集\n",
    "\n",
    "为了获得整个测试集的预测，我们只需要用它的输入函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "predictions = model.predict(input_fn=test_input_fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Input graph does not contain a QueueRunner. That means predict yields forever. This is probably a mistake.\n",
      "INFO:tensorflow:Restoring parameters from ./checkpoints_tutorial18-2/model.ckpt-200\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 2, 0, 0, 0, 2,\n",
       "       0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 2, 1, 0, 0, 2, 0, 0, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 1, 1, 0, 0, 0, 2,\n",
       "       2, 0, 0, 2, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 2, 0, 2, 0, 0, 0, 0, 0, 0,\n",
       "       0, 1, 1, 2, 1, 0, 0, 1, 2, 0, 0, 1, 0, 1, 1, 0, 0, 0, 2, 0, 1, 0, 0,\n",
       "       1, 0, 0, 1, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       2, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1,\n",
       "       1, 2, 1, 0, 1, 0, 0, 1, 1, 0, 2, 2, 0, 2, 1, 0, 1, 2, 0, 0, 1, 2, 1,\n",
       "       1, 0, 1, 0, 0, 1, 1, 0, 0, 2, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 2, 2, 0,\n",
       "       1, 0, 0, 1, 1, 0, 0, 2, 1, 0, 1, 0, 0, 1, 0, 0, 1, 2, 2, 0, 1, 1, 2,\n",
       "       1, 0, 0, 2, 2, 2, 0, 1, 1, 2, 2, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1,\n",
       "       2, 0, 1, 0, 1, 0, 2, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1,\n",
       "       1, 0, 2, 1, 2, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 1, 0, 2,\n",
       "       2, 2, 0, 0, 1, 1, 1, 0, 1, 2, 0, 0, 0, 2, 2, 0, 1, 0, 1, 1, 0, 0, 1,\n",
       "       0, 0, 1, 2, 0, 0, 1, 1, 1, 0, 1, 0, 1, 2, 0, 0, 0, 1, 0, 0, 1, 2, 1,\n",
       "       0, 2, 1, 1, 1, 1, 1, 0, 1, 2, 0, 0, 0, 1, 0, 2, 1, 0, 1, 1, 1, 1, 0,\n",
       "       0, 2, 1, 0, 1, 0, 2, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
       "       0])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_pred = np.array(list(predictions))\n",
    "cls_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "卷积神经网络对图像预测出不同类别，尽管大多数被分到了0类（叉子），所以正确率很糟糕。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "333"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(cls_pred == 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(cls_pred == 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "53"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(cls_pred == 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 总结\n",
    "\n",
    "在这个教程里展示了如何通过Dataset和Estimator API使用Tensorflow的二值格式文件TFRecords，通过获得高GPU使用率，简化了大量数据下来训练模型的过程。然而，这个API在很多方面都可以做的更简单。"
   ]
  },
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   "source": [
    "## 练习\n",
    "\n",
    "下面使一些可能会让你提升TensorFlow技能的一些建议练习。为了学习如何更合适地使用TensorFlow，实践经验是很重要的。\n",
    "\n",
    "在你对这个Notebook进行修改之前，可能需要先备份一下。\n",
    "\n",
    "* 训练卷积神经网络更久。它是否会在 Knifey-Spoony数据集上表现的更好一些？\n",
    "* 保存独热编码标签到TFRecord中来代替整形标签，修改其余的代码以使用它。\n",
    "* 制作成多个TFRecord文件。\n",
    "* 在TFRecord中保存JPEG文件来代替解码的图片。你之后需要在`parse()`函数中进行解码。What are the pro's and con's of doing this?\n",
    "* 尝试用另一个数据集。\n",
    "* 用一个图像大小不同的数据集。你是在转换成TFRecord文件之前还是之后将文件缩放？为什么？\n",
    "* 在利用Estimator API时尝试用numpy输入函数来代替TFRecords。性能会有什么不同？\n",
    "* 向朋友解释程序是如何工作的。"
   ]
  },
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   "metadata": {},
   "source": [
    "## License (MIT)\n",
    "\n",
    "Copyright (c) 2016-2017 by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n",
    "\n",
    "Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  }
 ],
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